CN113075569A - Battery state of charge estimation method and device based on noise adaptive particle filtering - Google Patents
Battery state of charge estimation method and device based on noise adaptive particle filtering Download PDFInfo
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Abstract
A battery state of charge estimation method and device based on noise adaptive particle filtering obtains monomer data sampled in a fixed period, and selects a characteristic monomer to perform parameter identification and state tracking; initializing parameters of a standard particle filter algorithm, and acquiring an SOC initial state particle set according to a target state estimated value and an initial noise covariance; performing one-step prediction of an SOC value, guiding sequential importance sampling by combining terminal voltage estimation deviation of particles, and updating normalization weight; weighting calculation is carried out to obtain a predicted value of the SOC, and particle set updating and new particle set measurement deviation recording are completed according to normalization weight random resampling; determining a noise standard deviation range of the adaptive process for the next cycle prediction; the loop execution completes the estimation of the SOC of the battery based on the noise adaptive particle filtering. The defect that the process noise variance is determined by multiple trial and error when the traditional particle filter method is applied to SOC estimation is overcome.
Description
Technical Field
The invention relates to the technical field of electric vehicle battery management, in particular to a battery state of charge estimation method and device based on noise adaptive particle filtering.
Background
With the increase of energy shortage and environmental pollution pressure, the popularization speed of pure electric vehicles and hybrid electric vehicles in life is greatly increased. The state of charge (soc) of the battery is one of the key state parameters of the electric vehicle, and accurate estimation of the soc is an important means for ensuring the normal operation of the vehicle. When an automobile runs continuously for a long time, a large Ah accumulated deviation is often brought, and a more complex prediction algorithm needs to be added to improve the precision of the real-time estimation of the SOC. However, the vehicle-mounted end battery management system BMS generally can only perform a relatively well-implemented algorithm in view of cost pressure and technical difficulty.
The rapid development of the intelligent networked automobile provides a solution for the contradiction, and the state of the battery can be evaluated and calibrated through cloud platform data. However, the cloud platform data has the characteristic of large sampling interval, so that a new Ah integral deviation is introduced into a one-step predicted value of the SOC, and meanwhile, the uncertainty of battery model parameter identification and state tracking based on the data is increased.
The particle filtering method is to use a random sampling point to approximate the probability density function of a system random variable, and to replace integral operation with a sample mean value, so as to obtain the minimum variance estimation of the state, and the particle filtering method is widely used in SOC estimation research. However, the conventional particle filtering method fixes process noise, and if the noise variance is too small, the deviation cannot be effectively converged, and if the noise variance is too large, obvious noise interference is easily introduced. When the method is applied to cloud platform data with large sampling intervals, noise interference in the state process is more obvious, the variance is often determined by adopting a particle filtering method with fixed noise through multiple trial and error, the application is inconvenient, and the SOC of the battery is difficult to stably track and predict.
Disclosure of Invention
The invention provides a battery state-of-charge estimation method and device based on noise adaptive particle filtering, and aims to solve the problems that multiple trial and error are needed to determine the process noise variance when the traditional particle filtering method for fixing process noise is applied to state-of-charge SOC estimation, the defects comprise time consumption, non-convergence is easily caused due to small size, and unstable state tracking is caused due to large size, so that the state tracking effect is poor, and the prediction robustness is reduced.
In order to solve the technical problem, the invention provides a battery state of charge estimation method based on noise adaptive particle filtering, which comprises the following steps:
step 1: acquiring voltage, current and temperature data of all monomers of the battery pack sampled in a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
step 2: initializing parameters of a standard particle filter algorithm, and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
and step 3: performing one-step prediction of a state of charge (SOC) value, guiding sequential importance sampling by combining terminal voltage estimation deviation of particles, and updating normalization weight of the particles;
and 4, step 4: calculating according to the normalized weight to obtain a predicted value of the SOC, and performing random resampling according to the normalized weight of the particles to complete updating of the particle set and recording of measurement deviation of a new particle set;
and 5: determining a self-adaptive process noise standard deviation range for one-step prediction of the SOC of the next cycle;
step 6: and (5) circularly executing the steps 3-5 within the data length to finish the SOC estimation of the battery based on the noise adaptive particle filter.
Further, the sampling period is 10 s.
Further, the preprocessing the data specifically includes: and extracting the sampling point data of the voltage, the current and the temperature of all the monomers, and rejecting and repairing abnormal data.
Further, the initialized parameters include a process noise variance Q, a measurement noise variance R, and state of charge SOC initial particles extracted by performing prior distribution P (X0) according to an initial noise variance P, where the measurement noise variance R is set according to the accuracy of the voltage sensor, X0 represents a state at time 0, P (X0) represents a prior distribution probability, and a value of the initial noise variance P is to cover a possible state of charge SOC true value within a range of 3 σ, where σ is a standard deviation.
Further, the further prediction of the SOC value of the state of charge, the guidance of the sequential importance sampling by combining the terminal voltage estimation deviation of the particles, and the updating of the particle weight specifically include:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk；
Where eta is the charge-discharge efficiency, I is the current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance, U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein, i refers to the ith particle, i is 1,2 …, N is the total number of particles;
(3) giving out the open circuit of the corresponding particle according to the one-step predicted value and the open circuit voltage curveVoltage UocAnd performing battery model parameter online identification based on recursive least square algorithm by combining current and voltage measurement values to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
p refers to the probability distribution.
Further, the weighting calculation according to the normalization weight is performed to obtain a predicted value of the state of charge SOC, and random resampling is performed according to the normalization weight of the particles to complete updating of the particle set and recording of measurement deviation of the new particle set, specifically:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
resampling the particles according to the normalized weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
Further, the determining an adaptive process noise standard deviation range for further prediction of the state of charge SOC of the next cycle specifically includes:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC), a fluctuation function, which represents fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC indicating a change amount of the state of charge SOC, Δ ocv indicating an open circuit voltage change amount;
(2) establishing a relation between the value of the process noise and the state tracking deviation of the updated particle set, and determining the process noise variance Qk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (1) specifically includes:
according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
Imaxrepresents the maximum allowable discharge current, Δ SOC, of the batterymaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, where Deltat represents the sampling interval, CnRepresents the battery capacity;
then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3；
(4) determination of Qk+1Upper limit of (2), wherein Qk+1＜＝Q0，Q0Is the process noise initial value;
(5) the process noise variance Q is obtained by integrating the stepsk+1The value satisfies:
(6) substituting variance as Qk+1For process noiseThe SOC of the next cycle is predicted in one step.
The invention also provides a battery state of charge estimation device based on the noise adaptive particle filter, which comprises a data acquisition unit, an initial state particle set acquisition unit, a one-step prediction and updating unit, an updating and recording unit, a range determination unit and an estimation unit;
the data acquisition unit is used for acquiring voltage, current and temperature data of all monomers of the battery pack sampled at a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
the initial state particle set acquisition unit is used for carrying out parameter initialization on a standard particle filter algorithm and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
the one-step prediction and updating unit is used for performing one-step prediction of the SOC value, guiding sequential importance sampling by combining terminal voltage estimation deviation of the particles and updating the normalized weight of the particles;
the updating and recording unit is used for obtaining a predicted value of the SOC according to the weighting calculation of the normalized weight, and performing random resampling according to the normalized weight of the particles to complete the updating of the particle set and the recording of the measurement deviation of the new particle set;
the range determining unit is used for determining a self-adaptive process noise standard deviation range and is used for one-step prediction of the state of charge (SOC) of the next cycle;
and the estimation unit is used for circularly executing the functions executed by the one-step prediction and updating unit, the updating and recording unit and the range determining unit in the data length to complete the SOC estimation of the battery based on the noise adaptive particle filter.
Further, the sampling period is 10 s.
Further, the preprocessing the data specifically includes: and extracting the sampling point data of the voltage, the current and the temperature of all the monomers, and rejecting and repairing abnormal data.
Further, the initialized parameters include a process noise variance Q, a measurement noise variance R, and state of charge SOC initial particles extracted by performing prior distribution P (X0) according to an initial noise variance P, where the measurement noise variance R is set according to the accuracy of the voltage sensor, X0 represents a state at time 0, P (X0) represents a prior distribution probability, and a value of the initial noise variance P is to cover a possible state of charge SOC true value within a range of 3 σ, where σ is a standard deviation.
Further, the further prediction of the SOC value of the state of charge, the guidance of the sequential importance sampling by combining the terminal voltage estimation deviation of the particles, and the updating of the particle weight specifically include:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk；
Where eta is the charge-discharge efficiency, I is the current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance, U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein, i refers to the ith particle, i is 1,2 …, N is the total number of particles;
(3) giving out the open-circuit voltage U of the corresponding particle according to the one-step predicted value and the open-circuit voltage curveocAnd performing battery model parameter online identification based on recursive least square algorithm by combining current and voltage measurement values to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
p refers to the probability distribution.
Further, the weighting calculation according to the normalization weight is performed to obtain a predicted value of the state of charge SOC, and random resampling is performed according to the normalization weight of the particles to complete updating of the particle set and recording of measurement deviation of the new particle set, specifically:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
resampling the particles according to the normalized weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
Further, the determining an adaptive process noise standard deviation range for further prediction of the state of charge SOC of the next cycle specifically includes:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC), a fluctuation function, which represents fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC indicating a change amount of the state of charge SOC, Δ ocv indicating an open circuit voltage change amount;
(2) establishing a relation between the value of the process noise and the state tracking deviation of the updated particle set, and determining the process noise variance Qk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (1) specifically includes:
according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
Imaxrepresents the maximum allowable discharge current, Δ SOC, of the batterymaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, where Deltat represents the sampling interval, CnRepresents the battery capacity;
then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3；
(4) determination of Qk+1Upper limit of (2), wherein Qk+1＜＝Q0，Q0Is the process noise initial value;
(5) the process noise variance Q is obtained by integrating the stepsk+1The value satisfies:
(6) substituting variance as Qk+1Process noise of (2) forOne-cycle SOC one-step prediction.
The present invention also provides an electronic device, including:
a storage device;
one or more processors;
the storage device is used to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the method.
Compared with the prior art, the invention has the following remarkable effective effects:
firstly, the method comprises the following steps: aiming at the defects that multiple trial and error are needed to determine the process noise variance when fixed process noise is applied to SOC estimation in a particle filter algorithm (time is consumed, the situation that the situation tracking is unstable due to small noise and large noise easily causes non-convergence), and further the situation tracking effect is poor and the prediction robustness is reduced, a noise adaptive adjustment method is provided for determining the process noise range.
Secondly, the method comprises the following steps: the self-adaptive noise adjusting method sets the upper limit of the noise value according to the fact that the noise coverage range cannot be too large, sets the lower limit of the noise value according to the Ah deviation determined by the data sampling interval, limits the noise range based on the algorithm principle and the data characteristics, avoids interference caused by accidental deviation, is more comprehensive in consideration, and can better fit the tracking process.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a plot of the fluctuation function of SOC with voltage deviation according to an embodiment of the present invention;
FIG. 3 illustrates the operating condition current of an embodiment of the present invention;
FIG. 4 shows the adaptive noise adjustment result according to an embodiment of the present invention;
FIG. 5 shows the SOC prediction results of an embodiment of the present invention;
FIG. 6 illustrates an SOC prediction bias according to an embodiment of the present invention;
fig. 7 is a block diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The particle filtering method uses random sampling points to approximate the probability density function of the system random variable, and uses the sample mean value to replace the integral operation, thereby obtaining the minimum variance estimation of the state. After particle initialization distribution and one-step prediction are carried out, the true value can be tracked rapidly and effectively in the range which can be covered by the particles, and the traditional particle filtering method selects the fixed process noise according to a large amount of statistical data or experience, so that the optimal noise value is not easy to obtain. And the state estimation deviation can be utilized to adaptively adjust the noise variance to guide the particle distribution by combining the fluctuation condition of the state of charge (SOC) along with the voltage and the known data fluctuation. Therefore, theoretically, the noise adaptive particle filter can effectively improve the state of charge (SOC) estimation stability.
Based on the theory, the invention provides a battery state of charge estimation method based on noise adaptive particle filtering. Firstly, acquiring voltage, current and temperature data of all monomers of a battery pack sampled in a fixed sampling period, selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking, and performing parameter initialization according to a traditional particle filter algorithm; secondly, performing particle one-step prediction and parameter identification based on a battery model, so as to guide an importance sampling process by using measured voltage and obtain weight distribution; secondly, calculating according to the weight to obtain a predicted value of the SOC, performing random resampling according to the weight of the particles to complete the updating of the particle set, and recording the measurement deviation of the new particle set for noise adjustment; and finally, substituting the off-line relation function according to the off-line obtained open-circuit voltage curve relation and the current temperature under different temperatures to obtain the updated process noise variance, and entering the next cycle prediction process.
The first embodiment is as follows:
referring to fig. 1, a first embodiment of the present invention provides a battery state of charge estimation method based on noise adaptive particle filtering, including the following steps:
step 1: acquiring voltage, current and temperature data of all monomers of the battery pack sampled in a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
the research object of the invention is the data of the series battery under the current excitation of the FTP running condition, and the sampling period is 10 s. Extracting sampling point data of voltage, current and temperature of all monomers, removing and repairing abnormal data, performing statistical analysis to select the monomer with the lowest voltage as a characteristic monomer as a subsequent algorithm application object, wherein the monomer with the lowest voltage is the monomer with the lowest occurrence frequency calculated according to statistical probability and connected with the battery pack in series under the discharge working condition.
Step 2: initializing parameters of a standard particle filter algorithm, and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
the initialized parameters comprise a process noise variance Q, a measurement noise variance R and state of charge SOC initial particles obtained by carrying out prior distribution P (X0) extraction according to an initial noise covariance P. The measurement noise variance R is set according to the accuracy of the voltage sensor, X0 represents a state at 0 moment, P (X0) represents prior distribution probability, and the value of the initial noise variance P can cover a possible SOC true value within a 3 sigma range, wherein sigma is a standard deviation, 3 sigma means that a target quantity conforms to a normal distribution rule, if the common accumulated deviation in continuous estimation is not more than 10%, P can be selected to be 0.01, and particle initialization within the distribution range is performed around the current prior SOC estimated value.
And step 3: performing one-step prediction of a state of charge (SOC) value, guiding sequential importance sampling by combining terminal voltage estimation deviation of particles, and updating normalization weight of the particles;
the one-step prediction of the SOC value is carried out, sequential importance sampling is guided by combining terminal voltage estimation deviation of particles, and particle weight is updated, and the method specifically comprises the following steps:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk。
Where eta is the charge-discharge efficiency, I is the current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance, U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein i is the ith particle, i is 1,2 …, N is the total number of particles, and f is the number not including ωkThe equation of state of (a).
(3) Giving out the open-circuit voltage U of the corresponding particle according to the one-step predicted value and the open-circuit voltage curveocCombining current and voltageThe measured value is subjected to battery model parameter online identification based on recursive least square algorithm to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
p denotes the probability distribution, wherein
The expression is the model terminal voltage output, which is the simulation quantity after the on-line identification parameters of the constructed measurement equation are substituted, and the simulation quantity refers to the simulation quantity of the voltage at two ends of the battery, and the above-mentioned y represents the real measured value of the voltage at two ends of the battery, which is measured by the sensor and is the real value.
And 4, step 4: calculating according to the normalized weight to obtain a predicted value of the SOC, and performing random resampling according to the normalized weight of the particles to complete updating of the particle set and recording of measurement deviation of a new particle set;
the method comprises the following steps of obtaining a predicted value of the SOC according to weight weighting calculation, and performing random resampling according to the normalized weight of the particles to update the particle set and record measurement deviation of a new particle set, and specifically comprises the following steps:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
according to the aboveCarrying out particle resampling by normalizing the weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
And 5: determining a self-adaptive process noise standard deviation range for one-step prediction of the SOC of the next cycle;
the determining of the adaptive process noise standard deviation range is used for one-step prediction of the state of charge (SOC) of the next cycle, and specifically comprises the following steps:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC) is a fluctuation function representing fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC is a change amount of the state of charge SOC, and Δ ocv is an open circuit voltage change amount.
Open-circuit voltage curves at different temperatures are measured off-line, 4-order polynomial fitting is carried out on the open-circuit voltage, first-order partial derivative is solved, and the SOC fluctuation caused by each mv voltage change at different SOC stages is obtained by taking the inverse.
The fluctuation function of this type of cell at 10 ℃ is shown in fig. 2.
(2) Determining process noise variance Qk+1A range of (d);
the determination process noise variance Qk+1Including:
considering the fluctuation of the particle range, the value of the process noise is linked with the updated state tracking deviation of the particle set, and the process noise variance Q is determinedk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (d);
as the sampling period of the data is 10s, obvious single-step Ah deviation can be introduced in the application of the SOC estimation algorithm according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
wherein, ImaxRepresents the maximum allowable discharge current, Δ SOC, of the batterymaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, at being the sampling interval, CnIndicating the battery capacity.
Then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3
(4) determination of Qk+1The upper limit of (d);
in the particle filter algorithm, the larger the process noise value is, the stronger the volatility of low-state tracking is generally, so that the Q needs to be limitedk+1And in practical application, the initial value Q of the process noise is obtained by a few trial and error for a specific object0Is a reasonable experience range, so the maximum value of the noise variance in the setting process should satisfy:
Qk+1＜＝Q0；
wherein Q0Is the process noise initial value.
(5) The process noise variance Q is obtained by integrating the stepsk+1Value satisfies
(6) Substituting variance as Qk+1Is used for the SOC one-step prediction for the next cycle.
Step 6: and (5) circularly executing the steps 3-5 within the data length to finish the SOC estimation of the battery based on the noise adaptive particle filter.
The data length refers to a data segment length that needs to be subjected to continuous state estimation, that is, how many time instants, the number of samples L, k is 0,1, …, L.
The following are the test results of this example:
the selected study object in the example is a battery pack under current excitation of FTP running condition, the sampling period is 10s, and the discharge current is shown in FIG. 3. Set Q0Applied to the example, Q, which is obtainable for state tracking according to steps 3-5, 0.0001(σ ═ 0.01)k+1The value adaptive adjustment effect is shown in fig. 4, and the state of charge SOC estimation effect and the absolute deviation of the proposed N-APF algorithm are shown in fig. 5 and fig. 6.
In summary, the present embodiment provides a method for estimating an SOC of an electric vehicle battery based on a noise adaptive particle filter. The effect of the embodiment shows that the method can effectively and dynamically adjust the process noise, thereby reducing the fluctuation of state tracking in the uncertain noise environment and ensuring the SOC estimation stability under the large-interval sampling condition.
Example two:
referring to fig. 7, a second embodiment of the present invention provides a battery state of charge estimation apparatus based on noise adaptive particle filtering, where the apparatus includes a data acquisition unit, an initial state particle set acquisition unit, a one-step prediction and update unit, an update and record unit, a range determination unit, and an estimation unit;
the data acquisition unit is used for acquiring voltage, current and temperature data of all monomers of the battery pack sampled at a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
the research object of the invention is the data of the series battery under the current excitation of the FTP running condition, and the sampling period is 10 s. Extracting sampling point data of voltage, current and temperature of all monomers, removing and repairing abnormal data, performing statistical analysis to select the monomer with the lowest voltage as a characteristic monomer as a subsequent algorithm application object, wherein the monomer with the lowest voltage is the monomer with the lowest occurrence frequency calculated according to statistical probability and connected with the battery pack in series under the discharge working condition.
The initial state particle set acquisition unit is used for carrying out parameter initialization on a standard particle filter algorithm and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
the initialized parameters comprise a process noise variance Q, a measurement noise variance R and state of charge SOC initial particles obtained by carrying out prior distribution P (X0) extraction according to an initial noise covariance P. The measurement noise variance R is set according to the accuracy of the voltage sensor, X0 represents a state at 0 moment, P (X0) represents prior distribution probability, and the value of the initial noise variance P can cover a possible SOC true value within a 3 sigma range, wherein sigma is a standard deviation, 3 sigma means that a target quantity conforms to a normal distribution rule, if the common accumulated deviation in continuous estimation is not more than 10%, P can be selected to be 0.01, and particle initialization within the distribution range is performed around the current prior SOC estimated value.
The one-step prediction and updating unit is used for performing one-step prediction of the SOC value, guiding sequential importance sampling by combining terminal voltage estimation deviation of the particles and updating the normalized weight of the particles;
the one-step prediction of the SOC value is carried out, sequential importance sampling is guided by combining terminal voltage estimation deviation of particles, and particle weight is updated, and the method specifically comprises the following steps:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk。
Wherein eta isCharge-discharge efficiency, I is current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance, U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein i is the ith particle, i is 1,2 …, N is the total number of particles, and f is the number not including ωkThe equation of state of (a).
(3) Giving out the open-circuit voltage U of the corresponding particle according to the one-step predicted value and the open-circuit voltage curveocAnd performing battery model parameter online identification based on recursive least square algorithm by combining current and voltage measurement values to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
p denotes the probability distribution, wherein
The expression is the model terminal voltage output, which is the simulation quantity after the on-line identification parameters of the constructed measurement equation are substituted, and the simulation quantity refers to the simulation quantity of the voltage at two ends of the battery, and the above-mentioned y represents the real measured value of the voltage at two ends of the battery, which is measured by the sensor and is the real value.
The updating and recording unit is used for obtaining a predicted value of the SOC according to the weighting calculation of the normalized weight, and performing random resampling according to the normalized weight of the particles to complete the updating of the particle set and the recording of the measurement deviation of the new particle set;
the method comprises the following steps of obtaining a predicted value of the SOC according to weight weighting calculation, and performing random resampling according to the normalized weight of the particles to update the particle set and record measurement deviation of a new particle set, and specifically comprises the following steps:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
resampling the particles according to the normalized weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
The range determining unit is used for determining a self-adaptive process noise standard deviation range and is used for one-step prediction of the state of charge (SOC) of the next cycle;
the determining of the adaptive process noise standard deviation range is used for one-step prediction of the state of charge (SOC) of the next cycle, and specifically comprises the following steps:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC) is a fluctuation function representing fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC is a change amount of the state of charge SOC, and Δ ocv is an open circuit voltage change amount.
Open-circuit voltage curves at different temperatures are measured off-line, 4-order polynomial fitting is carried out on the open-circuit voltage, first-order partial derivative is solved, and the SOC fluctuation caused by each mv voltage change at different SOC stages is obtained by taking the inverse.
The fluctuation function of this type of cell at 10 ℃ is shown in fig. 2.
(2) Determining process noise variance Qk+1A range of (d);
the determination process noise variance Qk+1Including:
considering the fluctuation of the particle range, the value of the process noise is linked with the updated state tracking deviation of the particle set, and the process noise variance Q is determinedk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (d);
as the sampling period of the data is 10s, obvious single-step Ah deviation can be introduced in the application of the SOC estimation algorithm according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
wherein, ImaxRepresents the maximum allowable discharge current, Δ SOC, of the batterymaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, at being the sampling interval, CnIndicating the battery capacity.
Then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3
(4) determination of Qk+1The upper limit of (d);
in the particle filter algorithm, the larger the process noise value is, the stronger the volatility of low-state tracking is generally, so that the Q needs to be limitedk+1And in practical application, the value obtained by a few trial and error processes for a specific objectInitial value of range noise Q0Is a reasonable experience range, so the maximum value of the noise variance in the setting process should satisfy:
Qk+1＜＝Q0；
wherein Q0Is the process noise initial value.
(5) The process noise variance Q is obtained by integrating the stepsk+1Value satisfies
(6) Substituting variance as Qk+1Is used for the SOC one-step prediction for the next cycle.
And the estimation unit is used for circularly executing the functions executed by the one-step prediction and updating unit, the updating and recording unit and the range determining unit in the data length to complete the SOC estimation of the battery based on the noise adaptive particle filter.
The data length refers to a data segment length that needs to be subjected to continuous state estimation, that is, how many time instants, the number of samples L, k is 0,1, …, L.
The present invention also provides an electronic device, including: a storage device; one or more processors; the storage device is for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements a method as described above.
It will be understood by those skilled in the art that all or part of the steps of the above methods may be implemented by a program instructing relevant hardware, the program may be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc., and the program stored in the readable storage medium may be executed by a processor to implement the corresponding functions of the above methods. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiments may be implemented in the form of hardware, and may also be implemented in the form of a software functional module.
It should be understood that the above detailed description of the embodiments is not to be taken as limiting the scope of the invention. Those skilled in the art, having the benefit of this disclosure, may effect alterations and modifications thereto, all without departing from the scope of the invention as defined by the appended claims.
Claims (16)
1. A battery state of charge estimation method based on noise adaptive particle filtering is characterized by comprising the following steps:
step 1: acquiring voltage, current and temperature data of all monomers of the battery pack sampled in a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
step 2: initializing parameters of a standard particle filter algorithm, and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
and step 3: performing one-step prediction of a state of charge (SOC) value, guiding sequential importance sampling by combining terminal voltage estimation deviation of particles, and updating normalization weight of the particles;
and 4, step 4: calculating according to the normalized weight to obtain a predicted value of the SOC, and performing random resampling according to the normalized weight of the particles to complete updating of the particle set and recording of measurement deviation of a new particle set;
and 5: determining a self-adaptive process noise standard deviation range for one-step prediction of the SOC of the next cycle;
step 6: and (5) circularly executing the steps 3-5 within the data length to finish the SOC estimation of the battery based on the noise adaptive particle filter.
2. The method of claim 1, wherein the sampling period is 10 s.
3. The method according to claim 1, wherein the preprocessing the data is specifically: and extracting the sampling point data of the voltage, the current and the temperature of all the monomers, and rejecting and repairing abnormal data.
4. The method of any one of claims 1-3, wherein the initialized parameters include process noise variance Q, measurement noise variance R, and state of charge SOC primary particles extracted according to an a priori distribution P (X0) of initial noise covariance P, wherein the measurement noise variance R is set according to voltage sensor accuracy, X0 represents a state at time 0, P (X0) represents a probability of the a priori distribution, and the initial noise covariance P is selected to cover a true value of a possible state of charge SOC within 3 σ, where σ is a standard deviation.
5. The method of claim 4, wherein the performing the one-step prediction of the SOC value, the guiding the sequential importance sampling by combining the terminal voltage estimation bias of the particle, and the updating the particle weight specifically comprises:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk；
Where eta is the charge-discharge efficiency, I is the current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance，U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein, i refers to the ith particle, i is 1,2 …, N is the total number of particles;
(3) giving out the open-circuit voltage U of the corresponding particle according to the one-step predicted value and the open-circuit voltage curveocAnd performing battery model parameter online identification based on recursive least square algorithm by combining current and voltage measurement values to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
p refers to the probability distribution.
6. The method according to claim 5, wherein the calculating according to the normalized weight to obtain the predicted value of the state of charge (SOC) and performing random resampling according to the normalized weight of the particles to complete the updating of the particle set and the recording of the measurement deviation of the new particle set, specifically:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
resampling the particles according to the normalized weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
7. The method of claim 6, wherein determining an adaptive process noise standard deviation range for the one-step prediction of the state of charge (SOC) for the next cycle comprises:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC), a fluctuation function, which represents fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC indicating a change amount of the state of charge SOC, Δ ocv indicating an open circuit voltage change amount;
(2) establishing a relation between the value of the process noise and the state tracking deviation of the updated particle set, and determining the process noise variance Qk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (1) specifically includes:
according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
Imaxrepresents the maximum allowable discharge current, Δ SO, of the batteryCmaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, where Deltat represents the sampling interval, CnRepresents the battery capacity;
then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3；
(4) determination of Qk+1Upper limit of (2), wherein Qk+1＜＝Q0，Q0Is the process noise initial value;
(5) the process noise variance Q is obtained by integrating the stepsk+1The value satisfies:
(6) substituting variance as Qk+1Is used for the SOC one-step prediction for the next cycle.
8. A battery state of charge estimation device based on noise adaptive particle filtering is characterized by comprising a data acquisition unit, an initial state particle set acquisition unit, a one-step prediction and updating unit, an updating and recording unit, a range determining unit and an estimation unit;
the data acquisition unit is used for acquiring voltage, current and temperature data of all monomers of the battery pack sampled at a fixed sampling period, preprocessing the data, and selecting the monomer with the lowest voltage as a characteristic monomer to perform parameter identification and state tracking;
the initial state particle set acquisition unit is used for carrying out parameter initialization on a standard particle filter algorithm and acquiring a SOC initial state particle set according to a target state estimated value and an initial noise covariance;
the one-step prediction and updating unit is used for performing one-step prediction of the SOC value, guiding sequential importance sampling by combining terminal voltage estimation deviation of the particles and updating the normalized weight of the particles;
the updating and recording unit is used for obtaining a predicted value of the SOC according to the weighting calculation of the normalized weight, and performing random resampling according to the normalized weight of the particles to complete the updating of the particle set and the recording of the measurement deviation of the new particle set;
the range determining unit is used for determining a self-adaptive process noise standard deviation range and is used for one-step prediction of the state of charge (SOC) of the next cycle;
and the estimation unit is used for circularly executing the functions executed by the one-step prediction and updating unit, the updating and recording unit and the range determining unit in the data length to complete the SOC estimation of the battery based on the noise adaptive particle filter.
9. The apparatus of claim 8, wherein the sampling period is 10 s.
10. The apparatus according to claim 8, wherein the preprocessing of the data is specifically: and extracting the sampling point data of the voltage, the current and the temperature of all the monomers, and rejecting and repairing abnormal data.
11. The apparatus of any of claims 8-10, wherein the initialized parameters comprise process noise variance Q, measurement noise variance R, and state of charge SOC initial particles extracted according to an a priori distribution P (X0) of initial noise covariance P, wherein the measurement noise variance R is set according to the voltage sensor accuracy, X0 represents the state at time 0, P (X0) represents the a priori distribution probability, and the initial noise covariance P is selected to cover the true value of the state of charge SOC within 3 σ, where σ is the standard deviation.
12. The apparatus of claim 11, wherein the performing of the one-step prediction of the SOC value, the guiding of the sequential importance sampling by combining the terminal voltage estimation deviation of the particle, and the updating of the particle weight specifically comprises:
(1) according to the Ah integral principle and the Davining equivalent circuit model, a state equation and a measurement equation of SOC estimation of the battery state of charge are obtained, wherein,
the state equation is:
the measurement equation is as follows: vk+1＝UOC(SOCk+1)-R0,k+1Ik+1-U1,k+1+vk；
Where eta is the charge-discharge efficiency, I is the current input, CnFor battery capacity, Δ t is the sampling interval, V is the terminal voltage output, UocIs an open circuit voltage, R0Is ohmic internal resistance, U1Is a polarization voltage, ωkAnd vkRespectively representing process noise and measurement noise, and k +1 represent time;
(2) when the time k is changed to the time k +1 from the initialization time k, a one-step predicted value of the SOC of the battery is obtained according to the state equation
Wherein, i refers to the ith particle, i is 1,2 …, N is the total number of particles;
(3) giving out the open-circuit voltage U of the corresponding particle according to the one-step predicted value and the open-circuit voltage curveocAnd performing battery model parameter online identification based on recursive least square algorithm by combining current and voltage measurement values to obtain model terminal voltage output
(4) Based on the measured value y of the terminal voltage of the batteryk+1Obtain the weight of each particle
Normalizing to obtain the normalized weight of each particle
Outline of p fingerAnd (4) rate distribution.
13. The apparatus according to claim 12, wherein the calculating according to the normalized weight to obtain the predicted value of the state of charge SOC, and performing random resampling according to the normalized weight of the particle to complete the updating of the particle set and the recording of the measurement deviation of the new particle set, specifically:
obtaining the predicted value of the SOC at the current moment according to the weighted calculation of the normalized weight
Wherein,
resampling the particles according to the normalized weight, and sequentially recording terminal voltage deviations corresponding to N particles obtained by resampling
Calculating the mean value thereof
14. The apparatus of claim 13, wherein determining an adaptive process noise standard deviation range for a one-step prediction of the state of charge (SOC) for a next cycle comprises:
(1) introducing a fluctuation function delta (SOC) of the state of charge SOC along with the open-circuit voltage at different temperatures, wherein:
δ(soc)＝Δsoc/Δocv；
δ (SOC), a fluctuation function, which represents fluctuation of the state of charge value per mv voltage change of the state of charge SOC at different SOC stages, Δ SOC indicating a change amount of the state of charge SOC, Δ ocv indicating an open circuit voltage change amount;
(2) summing the values of process noiseEstablishing relation between state tracking deviations of new particle sets and determining process noise variance Qk+1Satisfies the following conditions:
(3) determination of Qk+1The lower limit of (1) specifically includes:
according to the maximum allowable discharge current I of the batterymaxCalculating Δ SOCmaxWherein:
ΔSOCmax＝Imax·Δt/Cn；
Imaxrepresents the maximum allowable discharge current, Δ SOC, of the batterymaxRepresenting the single-step SOC integral deviation possibly caused by the maximum discharge current, where Deltat represents the sampling interval, CnRepresents the battery capacity;
then according to the 3 sigma principle, the minimum value of the process noise variance should satisfy:
sqrt(Qk+1)＞＝ΔSOCmax/3；
(4) determination of Qk+1Upper limit of (2), wherein Qk+1＜＝Q0，Q0Is the process noise initial value;
(5) the process noise variance Q is obtained by integrating the stepsk+1The value satisfies:
(6) substituting variance as Qk+1Is used for the SOC one-step prediction for the next cycle.
15. An electronic device, characterized in that the electronic device comprises:
a storage device;
one or more processors;
the storage device is for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
16. A computer-readable storage medium, on which a computer program is stored which, when executed, carries out the method according to one of claims 1 to 7.
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